The Parser project focuses on implementing a context-free grammar (CFG) parser to analyze English sentences and extract noun phrases. The goal is to build a tool that can interpret sentence structures and provide insights into their grammatical components.

• Context-Free Grammar Parsing: Utilizes CFG to decompose sentences into their grammatical elements.
• Noun Phrase Extraction: Identifies and extracts noun phrases based on CFG rules.
• Tokenization and Normalization: Processes sentences into tokens and normalizes them for consistent analysis.

• Python 3
• nltk library

1. Install NLTK:

   pip install nltk

2. Download Required NLTK Resources:

   import nltk
   nltk.download('punkt')

• parser.py: Main script implementing the CFG parser and noun phrase extraction.
• sentences/[sentence_file].txt: Text files containing sample sentences for parser testing.

1. Run the Parser:

   python parser.py sentences/1.txt

   • If a filename is specified, the script reads the sentence from that file.
   • If no filename is provided, the script will prompt for an input sentence.

2. Example Code:

   from parser import preprocess, np_chunk
   
   sentence = "The quick brown fox jumps over the lazy dog."
   tokens = preprocess(sentence)
   trees = list(parser.parse(tokens))
   for tree in trees:
       tree.pretty_print()
       for np in np_chunk(tree):
           print(" ".join(np.flatten()))

• Grammar Definitions: CFG rules are specified in TERMINALS and NONTERMINALS to represent sentence structure and parts of speech.
• Preprocessing: The preprocess function tokenizes the input sentence, converts it to lowercase, and filters out non-alphabetic words.
• Noun Phrase Chunking: The np_chunk function extracts noun phrases from the parse tree, avoiding nested noun phrases.

The parser provides visual representations of sentence structure and extracted noun phrases for various sample sentences.

For more information, visit the Parser Project.